Aiming at the problem that there are still much residues and block effect when removing high-density remote sensing image stripes

a new stripe separation method is proposed.The sparsity of stripe exists both in the spatial domain and gradient domain.Therefore

non-convex L0 norm constraint is applied to stripe sparsity in spatial domain and gradient domain

and L1 norm is adopted for constraining spatial continuity of clean images.The proposednon-convex model can be transformed into a convex optimization problem by mathematical programming of equilibrium constraint（MPEC） and solved quickly.The experimental results show that the proposed algorithm can remove high-density stripe noise

while retaining the structure and detail information of the original image

and is significantly prior to the state-of-art algorithms in terms of peak noise ratio（PSNR） and structural similarity index（SSIM）.